scholarly journals The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience

2022 ◽  
Vol 23 (2) ◽  
pp. 811
Author(s):  
Maiia E. Bragina ◽  
Antoine Daina ◽  
Marta A. S. Perez ◽  
Olivier Michielin ◽  
Vincent Zoete
Keyword(s):  
Virtual Screening ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Web Tool ◽  
Chemical Libraries ◽  
Compound Libraries ◽  
Rational Drug ◽  
Approved Drugs ◽  
3D Methods ◽  
2D And 3D

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

scholarly journals Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

2021 ◽  
Vol 9 ◽  
Author(s):  
Kauê Santana ◽  
Lidiane Diniz do Nascimento ◽  
Anderson Lima e Lima ◽  
Vinícius Damasceno ◽  
Claudio Nahum ◽  
...  
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Bioactive Compounds ◽  
Chemical Space ◽  
Structural Diversity ◽  
Industrial Applications ◽  
Bioactive Molecules ◽  
Natural Sources ◽  
Compound Libraries ◽  
Pharmacokinetic Properties

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

scholarly journals Virtual Screening and Free Energy Estimation for Identifying Mycobacterium Tuberculosis Flavoenzyme DprE1 Inhibitors

2020 ◽  
Author(s):  
Niranjan Kumar ◽  
Rakesh Srivastava ◽  
Amresh Prakash ◽  
Andrew M Lynn
Keyword(s):  
Free Energy ◽  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Energy Estimation ◽  
Chemical Libraries ◽  
Drug Molecules ◽  
Rational Drug ◽  
Drug Designing

<p>we investigated the promising MTB drug target protein, DprE1 (decaprenylphosphoryl-β-d-ribose 2’-epimerase), involve in cell was synthesis of Mycobacterium tuberculosis and plays a crucial role<b> </b>in host pathogenesis, virulence, lethality and survival under stress. Considering the emergence of different variants of drug resistant MTB are one of the major threats worldwide which essentially requires more effective new drug molecules with no major side effects. Here, we employed comprehensive computational methods for the structure based virtual screening of bioactive anti-tuberculosis compounds from chemical libraries ChEMBL, characterized the physicochemical properties analyses and the trajectories obtained from MD simulations were used for estimation of binding free energy, applying molecular theory of solvation (MM/PBSA, MM/GBSA AND MM/3DRISM-KH). All results were compared with known DprE1 inhibitors. Our studies suggest that four compounds (ChEMBL2441313, ChEMBL2338605, ChEMBL441373 and ChEMBL1607606) compounds may be explored as lead molecules for the rational drug designing of DprE1-inhibitors in MTB therapy.</p><br>

scholarly journals Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

2019 ◽  
Vol 26 (21) ◽  
pp. 3874-3889 ◽  
Author(s):  
Jelica Vucicevic ◽  
Katarina Nikolic ◽  
John B.O. Mitchell
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Antineoplastic Agents ◽  
Drug Targets ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Computational Approaches ◽  
Rational Drug

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Virtual Screening and Free Energy Estimation for Identifying Mycobacterium Tuberculosis Flavoenzyme DprE1 Inhibitors

2020 ◽  
Author(s):  
Niranjan Kumar ◽  
Rakesh Srivastava ◽  
Amresh Prakash ◽  
Andrew M Lynn
Keyword(s):  
Free Energy ◽  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Energy Estimation ◽  
Chemical Libraries ◽  
Drug Molecules ◽  
Rational Drug ◽  
Drug Designing

<p>we investigated the promising MTB drug target protein, DprE1 (decaprenylphosphoryl-β-d-ribose 2’-epimerase), involve in cell was synthesis of Mycobacterium tuberculosis and plays a crucial role<b> </b>in host pathogenesis, virulence, lethality and survival under stress. Considering the emergence of different variants of drug resistant MTB are one of the major threats worldwide which essentially requires more effective new drug molecules with no major side effects. Here, we employed comprehensive computational methods for the structure based virtual screening of bioactive anti-tuberculosis compounds from chemical libraries ChEMBL, characterized the physicochemical properties analyses and the trajectories obtained from MD simulations were used for estimation of binding free energy, applying molecular theory of solvation (MM/PBSA, MM/GBSA AND MM/3DRISM-KH). All results were compared with known DprE1 inhibitors. Our studies suggest that four compounds (ChEMBL2441313, ChEMBL2338605, ChEMBL441373 and ChEMBL1607606) compounds may be explored as lead molecules for the rational drug designing of DprE1-inhibitors in MTB therapy.</p><br>

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods

2010 ◽  
Vol 50 (12) ◽  
pp. 2079-2093 ◽  
Author(s):  
Vishwesh Venkatraman ◽  
Violeta I. Pérez-Nueno ◽  
Lazaros Mavridis ◽  
David W. Ritchie
Keyword(s):  
Virtual Screening ◽  
Screening Tools ◽  
Data Set ◽  
Comprehensive Comparison ◽  
3D Methods

scholarly journals Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches

2020 ◽  
Author(s):  
Alex Zhavoronkov ◽  
Vladimir Aladinskiy ◽  
Alexander Zhebrak ◽  
Bogdan Zagribelnyy ◽  
Victor Terentiev ◽  
...  
Keyword(s):  
Homology Modelling ◽  
Drug Repurposing ◽  
Hiv Protease ◽  
Learning Approaches ◽  
Protein Targets ◽  
Chemical Libraries ◽  
Internal Resources ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Novel Drug

<div> <div> <div> <p>The emergence of the 2019 novel coronavirus (2019-nCoV), for which there is no vaccine or any known effective treatment created a sense of urgency for novel drug discovery approaches. One of the most important 2019-nCoV protein targets is the 3C-like protease for which the crystal structure is known. Most of the immediate efforts are focused on drug repurposing of known clinically-approved drugs and virtual screening for the molecules available from chemical libraries that may not work well. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease is approximately 50 micromolar. In an attempt to address this challenge, on January 28th, 2020 Insilico Medicine decided to utilize a part of its generative chemistry pipeline to design novel drug-like inhibitors of 2019-nCoV and started generation on January 30th. It utilized three of its previously validated generative chemistry approaches: crystal-derived pocked- based generator, homology modelling-based generation, and ligand-based generation. Novel druglike compounds generated using these approaches are being published at www.insilico.com/ncov-sprint/ and will be continuously updated. Several molecules will be synthesized and tested using the internal resources; however, the team is seeking collaborations to synthesize, test, and, if needed, optimize the published molecules. </p> </div> </div> </div>

scholarly journals Mulberrofuran G, a Potent Inhibitor of SPIKE Protein of SARS Corona Virus 2

2021 ◽  
Author(s):  
Fatemeh Sadat Hosseini ◽  
Mohammad Reza Motamedi
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Active Site ◽  
Potential Role ◽  
Potent Inhibitor ◽  
Screening Method ◽  
Spike Protein ◽  
Treatment Agent ◽  
Approved Drugs ◽  
Fda Approved Drugs

Background: At the onset of the 2020 year, Coronavirus disease (COVID-19) has become a pandemic and infected many people worldwide. Despite all efforts, no cure was found for this infection. Bioinformatics and medicinal chemistry have a potential role in the primary consideration of drugs to treat this infection. With virtual screening and molecular docking, some potent compounds and medications can be found and modified and then applied to treat disease in the next steps. Methods: By virtual screening method and PRYX software, some Food and Drug Administration (FDA) approved drugs and natural compounds have been docked with the SPIKE protein of SARS-CoV-2. Some more potent agents have been selected, and then new structures are designed with better affinity than them. After that, we searched for the molecules with a similar structure to designed compounds to find the most potent compound to our target. Results: Because of the study of structures and affinities, mulberrofuran G was the most potent compound in this study. The compound has interacted strongly with residues in the probably active site of SPIKE. Conclusion: Mulberrofuran G can be a treatment agent candidate for COVID-19 because of its good affinity to SPIKE of the virus and inhibition of virus-cell adhesion and entrance.

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